Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine

Abstract The seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from > 1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) titer levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, vaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for > 75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the major determinants of seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants and an effect of vaccination month. BMI had a surprisingly small effect, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification.


Table of contents
. Impact of prevaccination history on seroconversion 2 Figure S2. Intercorrelations between all priors. 3 Figure S3. Observed vs. predicted values for Seroconversion and BaselineSY, respectively 4 Figure S4. Predicting Seroconversion without using the baseline HAI titer levels. 5 Figure S5. Predicting Seroconversion for people that only participated once in the UGA study. 6 Figure S6. Predicting Seroconversion for events arising from first-time and second-time participation for participants who participated twice or more in the UGA study 7 Figure S7. Cross-reactivity of antibodies built against different vaccine strains from the same subtype. 8 Figure S8. Predicting Seroconversion in Adult1 with data that fall within the first three months of the 9 five flu seasons Table Legends   Table S1   Table S2   Table S3   Table S4   Table S5

Figure S2. Intercorrelations between all priors.
The table shows correlations between all variables that are included in the modeling. Age and BMI are log2-transformed, and the categorical variables are transformed into numeric values first, then Pearson's correlation is calculated between the variables. *, P<0.05; **, P<0.01; ***, P<0.001. Plots along the diagonal are distributions of the variables. The red lines in the correlation plots below the diagonal are loess regression lines. Cmbdts: comorbidities; Prevacc: prevaccination; Vacc: vaccination; BL: baseline -here composite baseline.   A. Observed and predicted seroconversion for one-time participants in three subpopulations. B. Observed and predicted seroconversion for the same number of randomly selected vaccination events in three subpopulations.

Figure S6. Predicting Seroconversion for events from first-time and second-time participation for participants who participated twice or more in the UGA study
For the same participants who participated twice or more in the UGA study. A. Observed and predicted seroconversion in response to the first vaccination. Red dots depict 'naive' participants, i.e. those without vaccination in the three years prior to the vaccination event shown here. Cyan dots depict 'pre-vaccinated' participants, i.e. those with vaccination in the year prior to the vaccination event shown here B. Observed and predicted seroconversion in response to vaccination in the subsequent year. All participants, by definition, are prevaccinated, as they received the vaccine through the UGA study in the year before.

Appendix Table Legends
Table S1. Contributions of individual priors for Seroconversion and BaselineSY prediction, respectively.
The table shows the data underlying The table shows the frequencies of the top three frequent comorbidities in three subpopulations and their effects on seroconversion. The first column is subpopulation, second column the top three frequent comorbidities, third column the number of data entries for participants with this comorbidity, fourth column the total number of vaccination events, and fifth column the frequency of each comorbidity in each subpopulation. The sixth column is the comparison of "corrected seroconversion" (or residual seroconversion), i.e., observed minus predicted seroconversion, which represents the serological response after controlling for all the other factors, between participants with each of the listed comorbidities and those without any comorbidities, and seventh column the comparison of "corrected seroconversion" between participants with each of the listed comorbidities and those with other comorbidities other than the listed three, in each subpopulation.

Table S4. D28 HAI titer levels of participants in UGA4 who had the influenza infection information recorded in UGA5
We tested the difference in D28 HAI titer levels against A/H1N1 or A/H3N2 between 3 A positive and 14 negative participants and the p-values are 0.002 and 0.35 respectively. We also tested the difference in D28 HAI titer levels against B/Yam or B/Vic between 2 B positive and 14 negative participants and the p-values are 0.53 and 0.27 respectively. In summary, a significant difference is detected between the D28 HAI titer levels against A/H1N1 between flu A positive and negative participants.

Table S5. Parameters used to calculate the power of the modeling results for Seroconversion and BaselineSY predictions in three subpopulations
Seroconversion and BaselineSY are the composite metrics here. For each subpopulation, we used the number of unique participants in the training set as the sample size, so the power value we obtained is conserved because we used vaccination events in the prediction. The predictors are those retained in each model shown in Table S1. Effect size here is defined as R 2 /(1-R 2 ), and we set the significance level to be 0.05.